With vast improvements in computing power in the last two decades, humans have invested significantly in engineering resources in an attempt to automate labor intensive or dangerous tasks. A particularly dangerous and labor-intensive task is painting lines on roads for facilitating urban mobility. This thesis proposes an approach to automate the process of painting lines on the ground using an autonomous ground vehicle (AGV) fitted with a stabilized painting mechanism. The AGV accepts Global Positioning System (GPS) coordinates for waypoint navigation. A computer vision algorithm is developed to provide vision feedback to stabilize the painting mechanism. The system is demonstrated to follow an input desired trajectory and cancel any high frequency vibrations due to the uneven terrain that the vehicle is traversing. Also, the stabilizing system is able to eliminate the long-term drift (due to inaccurate GPS waypoint navigation) using the complementary vision system. / MS / There is a need to develop an automated system capable of painting lines on the ground with minimal human intervention as the current methods to paint lines on the ground are inefficient, labor intensive, and dangerous. The human input to such a system is limited to the determination of the desired trajectory of the line to be drawn. This thesis presents the design and development of an autonomous line painting system that includes an autonomous ground vehicle (capable of following GPS waypoints) integrated with an automatic line painting mechanism. As the vehicle traverses the ground, it experiences disturbances due to the interaction between the wheels and the ground, and also a long-term drift due to inaccurate tracking of the input GPS coordinates. In order to compensate for these disturbances, a vision system is implemented providing feedback to a stabilizing arm. This automated system is able to demonstrate the capability to follow a square trajectory defined by GPS coordinates while compensating for the disturbances.

This research investigates the possibility of controlling a simple biped having two degrees of freedom only. The biped robot walked on large feet. Having large feet enabled the robot to stand on one leg stably. At any time, the robotà ­s center of gravity remained above the area covered by one of the feet. Two servos actuated the two degrees of freedom tilting the robot to the side or moving the legs forward and backward. The biped moved by alternately tilting and striding. Turns were produced by dragging the feet along the ground. As the feet dragged, the friction generated under the feet created a turning moment that rotated the robot. Thus, the robot was able to step and turn on a flat surface. A control algorithm was developed to attempt trajectory tracking with the biped. Trajectories along a surface can be defined in terms of linear and angular velocities. In this research, it was assumed that a high level controller had transformed a desired trajectory into discrete steps of linear and angular velocities. Motion tests showed how various settings of the servos affected the step length and turning angle of the robot. To produce the desired velocities, a program was created to select the servo commands and set the speed parameters. This program applied knowledge of the expected step length and turning angle and performed feedforward control of the velocities. This investigation identified a trajectory tracking scheme that could be used in an observer feedback scenario to achieve accurate control. / Master of Science

This study addresses the limitations of existing autonomous vehicle datasets, particularly the need for greater specificity of weather conditions and road types. We utilized X-CAR to highlight the challenges of extreme weather and non-urban road conditions on autonomous driving systems. Our dataset comprises recordings under seven distinct weather and lighting conditions across four road types. Notably, our research focuses on differentiating between various lighting and weather conditions and road types, which often need improvement in many existing datasets. We used the X-CAR platform to collect 360-degree image information and LiDAR point clouds at 10Hz. Due to the constraints of time and resources, we used algorithmic prediction to generate ground truth data via the Co-DETR 2D prediction algorithm. We validated the accuracy of the Co-DETR algorithm through partial manual annotation. However, it is undeniable that in some extreme conditions, the algorithm-generated ground truth can lead to results deviating from expectations and real-world situations. Therefore, we conducted a scaled manual annotation and controlled experiments, ensuring the highest level of accuracy. After the manual annotation, we validated our initial conclusions and trained a model based on YOLOv8x, focusing on weak environmental conditions. The final model underwent multiple iterations and achieved satisfactory accuracy. The enhanced model demonstrated a significant increase in detection accuracy compared to the original YOLOv8x model. At the same time, our analysis identifies weather conditions that markedly reduce detection accuracy, providing focal points for future dataset enhancements. / Master of Science / This study explores the limitations of current autonomous vehicle datasets, particularly their lack of detail regarding weather conditions and road types. We used X-CAR to examine how extreme weather and light conditions affect autonomous driving systems. Our dataset includes recordings from seven different weather and lighting conditions across four types of roads. Due to time and resource constraints, we used an algorithm to predict ground truth data with the help of Co-DETR. While not all data was fully annotated, we manually labeled part of the data to create an actual ground truth. This allowed us to verify our previous findings and train a model based on YOLOv8x, focusing on challenging conditions. The improved model showed much higher accuracy in detecting objects than the original YOLOv8x model. This study highlights the significant impact of weather conditions on detection accuracy and suggests areas for future improvements in datasets.

Small unmanned aircraft systems (UAS) must be able to detect and avoid conflicting traffic, an especially challenging task when the threat is another small UAS. Collision avoidance requires trajectory prediction and the performance of a collision avoidance system can be improved by extending the prediction horizon. In this thesis, an algorithm for predicting the trajectory of a small, fixed-wing UAS using an estimate of its orientation and for maneuvering around the threat, if necessary, is developed. A computer vision algorithm locates specific feature points of a threat aircraft in an image and the POSIT algorithm uses these feature points to estimate the pose (position and attitude) of the threat. A sequence of pose estimates is then used to predict the trajectory of the threat aircraft and to avoid colliding with it. To assess the algorithm's performance, the predictions are compared with predictions based solely on position estimates for a variety of encounter scenarios. Simulation and experimental results indicate that trajectory prediction using orientation estimates provides quicker response to a change in the threat aircraft trajectory and results in better prediction and avoidance performance. / Master of Science / Small unmanned aircraft systems (UAS) must be able to detect and avoid conflicting traffic, an especially challenging task when the threat is another small UAS. Collision avoidance requires trajectory prediction and the performance of a collision avoidance system can be improved by extending the prediction horizon. In this thesis, an algorithm for predicting the trajectory of a small, fixed-wing UAS using an estimate of its orientation and for maneuvering around the threat, if necessary, is developed. A computer vision algorithm locates specific feature points of a threat aircraft in an image and a pose (position and attitude) estimation algorithm uses these feature points to estimate the pose of the threat. A sequence of pose estimates is then used to predict the trajectory of the threat aircraft and to avoid colliding with it. To assess the algorithm’s performance, the predictions are compared with predictions based solely on position estimates for a variety of encounter scenarios. Simulation and experimental results indicate that trajectory prediction using orientation estimates provides quicker response to a change in the threat aircraft trajectory and results in better prediction and avoidance performance.

Unmanned vehicles are often used in time-critical missions such as reconnaissance or search and rescue. To this end, this thesis provides autonomous localization and mapping tools for 1/R² sources. A "1/R²" source is one in which the received intensity of the source is inversely proportional to the square of the distance from the source. An autonomous localization algorithm is developed which utilizes a particle swarm particle ltering method to recursively estimate the location of a source. To implement the localization algorithm experimentally, a command interface with Virginia Tech's autonomous helicopter was developed. The interface accepts state information from the helicopter, and returns command inputs to drive the helicopter autonomously to the source. To make the use of the system more intuitive, a graphical user interface was developed which provides localization functionality as well as a waypoint navigation outer-loop controller for the helicopter. This assists in positioning the helicopter and returning it home after the the algorithm is completed. An autonomous mapping mission with a radioactive source is presented, along with a localization experiment utilizing simulated sensor readings. This work is the rst phase of an on-going project at the Unmanned Systems Lab. Accordingly, this thesis also provides a framework for its continuation in the next phase of the project. / Master of Science

The Virginia Tech self-mooring autonomous underwater vehicle (AUV) is capable of mooring itself on the seafloor for extended periods of time. The AUV is intended to travel to a desired mooring location, moor itself on the seafloor, and then release the mooring and return to a desired egress location. The AUV is designed to be an inexpensive sensor platform. The AUV utilizes a false nose that doubles as an anchor. The anchor is neutrally buoyant when attached to the AUV nose. When the vehicle moors it releases the false nose, which floods the anchor making it heavy, sinking both the anchor and AUV to the seafloor. At the end of the mooring time the vehicle releases the anchor line and travels to the recovery location. A prototype vehicle was constructed from a small-scale platform known as the Virginia Tech 475 AUV and used to test the self-mooring concept. The final self-mooring AUV was then constructed to perform the entire long duration mission. The final vehicle was tested successfully for an abbreviated mission profile. This report covers the general design elements of the self-mooring AUV, the detailed design of both the prototype and final AUVs, and the results of successful field trials with both vehicles. / Master of Science

Autonomous mobile robots (AMRs) use decentralized, AI-driven decision-making processes to providing material handling capabilities in industrial settings. Essay 1 examines how firms organize and engage to mitigate uncertainty during external technology integration (ETI), using an abductive approach with dyadic customer-supplier data to extend prior ETI models by exploring firm engagement, organizational adaptation, and distinct uncertainty types in AMR ETI projects. Essay 2 applies a grounded theory approach to examine AMR integration, using constant comparison and theoretical sampling to develop core categories explaining how suppliers, customers, and users exchange knowledge impacting AMR integration and project performance. Finally, Essay 3 is a conceptual paper examining the importance of end-user adoption by integrating ETI and technology acceptance model (TAM) frameworks, exploring important relationships between managerial interventions, cognitive constructs, user acceptance, and project success in AMR ETIs. As a whole, these essays contribute to the body of knowledge by extending the breadth and depth of current ETI models, emerging a substantive theory of AMR AIU, and extending TAM by grounding managerial interventions and individual cognitive constructs in an AMR context. Managers can use these frameworks to differentiate AMRs and other autonomous collaborative technology from traditional automation, and develop strategies enabling timely and effective AMR implementation.

The main research objective of this dissertation is to understand the sensing and communication challenges to achieving cooperative perception among autonomous vehicles, and then, using the insights gained, guide the design of the suitable format of data to be exchanged, reliable and efficient data fusion algorithms on vehicles. By understanding what and how data are exchanged among autonomous vehicles, from a machine learning perspective, it is possible to realize precise cooperative perception on autonomous vehicles, enabling massive amounts of sensor information to be shared amongst vehicles. I first discuss the trustworthy perception information sharing on connected and autonomous vehicles. Then how to achieve effective cooperative perception on autonomous vehicles via exchanging feature maps among vehicles is discussed in the following. In the last methodology part, I propose a set of mechanisms to improve the solution proposed before, i.e., reducing the amount of data transmitted in the network to achieve an efficient cooperative perception. The effectiveness and efficiency of our mechanism is analyzed and discussed.

published_or_final_version / Earth Sciences / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Earth Sciences / Master / Master of Philosophy